The Benefits of Personalized Data Mining Approaches to Human Activity Recognition with Smartphone Sensor Data
Abstract
Activity recognition allows ubiquitous mobile devices like smartphones to be context-aware and also enables new applications, such as mobile health applications that track a user's activities over time. However, it is difficult for smartphone-based activity recognition models to perform well, since only a single body location is instrumented. Most research focuses on universal/impersonal activity recognition models, where the model is trained using data from a panel of representative users. In this paper we compare the performance of these impersonal models with those of personal models, which are trained using labeled data from the intended user, and hybrid models, which combine aspects of both types of models. Our analysis indicates that personal training data is required for high accuracybut that only a very small amount of training data is necessary. This conclusion led us to implement a self-training capability into our Actitracker smartphone-based activity recognition system[1], and we believe personal models can also benefit other activity recognition systems as well.
Subject Area
Information science|Computer science
Recommended Citation
Lockhart, Jeffrey William, "The Benefits of Personalized Data Mining Approaches to Human Activity Recognition with Smartphone Sensor Data" (2014). ETD Collection for Fordham University. AAI1568376.
https://research.library.fordham.edu/dissertations/AAI1568376
